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A modified knowledge-based filtering method that uses case-based reasoning applied in real-estate recommendation system / Ronquillo, Lance Gerard V, Villa Gabriel C. 6

By: Ronquillo, Lance Gerard V, Villa Gabriel C. 4 0 16, [, ] | [, ] |

Contributor(s): 5 6 [] |

Language: Unknown language code Summary language: Unknown language code Original language: Unknown language code Series: ; June 2023.46Edition: Description: 28 cm. ix, 76 ppContent type: text Media type: unmediated Carrier type: volumeISBN: ISSN: 2Other title: 6 []Uniform titles: | | Related works: 1 40 6 []Subject(s): -- 2 -- 0 -- -- | -- 2 -- 0 -- 6 -- | 2 0 -- | -- -- 20 -- | | -- -- -- -- 20 -- | -- -- -- 20 -- --Genre/Form: -- 2 -- Additional physical formats: DDC classification: | LOC classification: | | 2Other classification:

Contents:

Action note: In: Summary: ABSTRACT: Since the Information Age came, a tremendous quantity of information has been made available online. The data may consist of general information, different fields of study, or even E-Commerce. The immense quantity of information often leads to a phenomenon known as information overload. The phenomenon led to the creation, development, and enhancement of different types of recommendation systems, Knowledge-Based Recommendation System (KBRS) suffers significantly in its performance since KBRS relies on user input and does not use on user input and does not use other user preferences such as liked, visited and trends. This study proposes an enhancement of the result retrieval process in the KBRS method that uses Cade-Based Reasoning. The aim is to improve the recommendation process using Feature Weighting, Feature Normalization, Weighted Cosine, and Percentile Concepts based on a study conducted by Knowledge/Domain Experts in real-estate recommendation systems. The results demonstrate significant improvements in performance metrics such as Precision and NDCG, providing directions for future studies and practical implications in enhancing user satisfaction and engagement. The additional results from the Percentile Concept also give a way to introduce novel cases into the recommendation list, thus offering a new dimension to the personalization and effectiveness of KBRS. Other editions:

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Item type	Current location	Home library	Collection	Call number	Status	Date due	Barcode	Item holds
Book	PLM	PLM Filipiniana Section	Filipiniana-Thesis	QA76.9.A43 R66 2023 (Browse shelf)	Available		FT7733

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Undergraduate Thesis: (Bachelor of Science in Information Technology), Pamantasan ng Lungsod ng Maynila, 2023. 56

ABSTRACT: Since the Information Age came, a tremendous quantity of information has been made available online. The data may consist of general information, different fields of study, or even E-Commerce. The immense quantity of information often leads to a phenomenon known as information overload. The phenomenon led to the creation, development, and enhancement of different types of recommendation systems, Knowledge-Based Recommendation System (KBRS) suffers significantly in its performance since KBRS relies on user input and does not use on user input and does not use other user preferences such as liked, visited and trends. This study proposes an enhancement of the result retrieval process in the KBRS method that uses Cade-Based Reasoning. The aim is to improve the recommendation process using Feature Weighting, Feature Normalization, Weighted Cosine, and Percentile Concepts based on a study conducted by Knowledge/Domain Experts in real-estate recommendation systems. The results demonstrate significant improvements in performance metrics such as Precision and NDCG, providing directions for future studies and practical implications in enhancing user satisfaction and engagement. The additional results from the Percentile Concept also give a way to introduce novel cases into the recommendation list, thus offering a new dimension to the personalization and effectiveness of KBRS.

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